[Data Meetup] Data Science in Finance - Building a Quant ML pipeline
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Georgi Kirov shares a common market-neutral statistical arbitrage framework. It will help showcase the many different ways to structure a systematic research project. From data reconciliation and signal backtesting to optimization and execution, what are some principled ways to evaluate and compare ML ideas? This process inevitably depends on the characteristics of a specific strategy, for instance, if it is liquidity-taking or liquidity-making.
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[Data Meetup] Data Science in Finance - Building a Quant ML pipeline
- 1. Building a Quant Research Pipeline from Scratch SOME LESSONS LEARNED IN BATTLE GEORGI KIROV, CASI-SOFIA UNIVERSITY GEORGI@KIROV.EU
- 2. Goals of this talk * Depends on ICE, Ezpada requirements
- 3. Systematic Trading Research Something happens somewhere in the world Trading Decision Profit
- 4. Systematic Trading Research ML & Data Science? Profit?
- 5. We are not interested in the big spike Rather, in everything else
- 6. 1. Infrastructure Matters Live Data Feed Data Handler ICE Europe l1 Signal 1, Brent Front Month Signal 2, Brent Front Month Optimizer, Brent Front Month Executor Data Handler ICE US l1 Signal 1, WTI Optimizer, WTI Executor Data Handler ICE US l2 Signal 2, WTI
- 7. Backtest according to your infrastructure Backfill Data Format ICE Europe l1 Signal 1, Brent Front Month Signal 2, Brent Front Month Optimizer, Brent Front Month Backtester Data Format ICE US l1 Signal 1, WTI Optimizer, WTI Backtester Data Format ICE US l2 Signal 2, WTI MODELING CAN BE USEFUL HERE
- 8. CORE DEVELOPMENT Low-level programming System architecture Model Integration Model Recalibration Data Storage Startup, Drop copy, Order Gateways Data cleaning Research environment Strategy research ML & Data Science Optimization Risk Management Reconciliation Quant Funds: an overview • Back Office: • Monitoring • Management • Monitoring • Networking/Hardwar e • Legal/Compliance • Brokers • Data Service Providers STRATEGY RESEARCH OPERATIONS
- 9. CORE DEVELOPMENT Low-level programming System architecture Model Integration Model Recalibration Data Storage Startup, Drop copy, Order Gateways Data cleaning Research environment Strategy research ML & Data Science Optimization Risk Management Reconciliation The field has leveled a bit • Networking/Hardwar e • Optiver/Cloud Infra • Brokers • Interactive Brokers • Lime • Data Service Providers • Quandl, AlgoSeek • Vela, CQG, TickData • IDS, TREP STRATEGY RESEARCH OPERATIONS
- 10. 2. Good Baselines > Fancy Models Consider a simple equity model regressed on market, sector, industry: 𝜀𝑡 𝑀𝑆𝐼 = 𝛽𝑡 𝑀𝑆𝐼 𝑥𝑡 − 𝑎 − 𝑦𝑡 Simple quadratic risk function: 𝑈(𝜔) = 𝜔 − 𝑏𝜔2 • Investigate liquidity-making vs liquidity-taking versions • When does 𝜀𝑡 𝑀𝑆𝐼 mean-revert best? • What are the best performance measures ? • PnL • Sharpe • Maximum Drawdown • Risk Profile SPEND A FEW MONTHS TO DOUBLE THE PERFORMANCE OF THIS TOY MODEL: GOOGLE’S ML RULES ARE CURIOUSLY APPLICABLE HERE
- 11. High Frequency Trading OPTIMIZED FOR EXECUTION • Model less than 1 tick • Many sophisticated competitors: huge barriers of entry OPTIMIZED FOR STATISTICS • Statistical models predict markets direction under a single tick • Go through the spread on the other side to do execution • Past and present market data drive trade decisions • Really optimized code, firms own radio tower links between exchanges
- 12. Statistical Arbitrage • Stat Arb does not depend tightly on market microstructure: • Slower ‘time to payoff’ • Make time work for you: • Invest in assets with positive carry (‘sail with the wind’) • Hedge partially by holding related assets. • Minimize risks in order to survive intermediate shocks.
- 13. 3. Backtesting should be forward-looking • Only actual forward performance matters • Realistic Backtesting is a sequential problem: • 𝑝𝑜𝑠𝑡, the desired position at time 𝑡 depends on transaction costs, b/a spread and 𝑝𝑜𝑠𝑡−1 • Even with perfect execution, summing up requires a massive convolution : unstable, difficult to efficiently compute • Slippage… Source: SMBC Comics
- 14. Backtesting should stop early At the very least, use a fixed cross-validation cycle: Monthly, weekly, daily retraining Training Performance ValidationPerformance
- 15. Wrapping up 1. INFRASTRUCTURE MATTERS 2. GOOD BASELINES > FANCY MODELS 3. FORWARD-LOOKING BACKTESTING Source: XKCD